APIs Benchmark Scripts 
AWS Machine Learning
JULY 2, 2024
You can see that for the 45 models we benchmarked, there is a 1.35x latency improvement (geomean for the 45 models). You can see that for the 33 models we benchmarked, there is around 2x performance improvement (geomean for the 33 models). We benchmarked 45 models using the scripts from the TorchBench repo.
AWS Machine Learning
OCTOBER 21, 2024
This feature empowers customers to import and use their customized models alongside existing foundation models (FMs) through a single, unified API. Having a unified developer experience when accessing custom models or base models through Amazon Bedrock’s API. Ease of deployment through a fully managed, serverless, service. 2, 3, 3.1,
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AWS Machine Learning
NOVEMBER 30, 2023
SageMaker makes it easy to deploy models into production directly through API calls to the service. It’s a low-level API available for Java, C++, Go, JavaScript, Node.js, PHP, Ruby, and Python. It’s a low-level API available for Java, C++, Go, JavaScript, Node.js, PHP, Ruby, and Python.
Spearline
JULY 21, 2022
And testingRTC offers multiple ways to export these metrics, from direct collection from webhooks, to downloading results in CSV format using the REST API. Flip the script With testingRTC, you only need to write scripts once, you can then run them multiple times and scale them up or down as you see fit. Happy days!
AWS Machine Learning
JULY 26, 2023
We compile the UNet for one batch (by using input tensors with one batch), then use the torch_neuronx.DataParallel API to load this single batch model onto each core. Load the UNet model onto two Neuron cores using the torch_neuronx.DataParallel API. If you have a custom inference script, you need to provide that instead.
AWS Machine Learning
FEBRUARY 6, 2023
The former question addresses model selection across model architectures, while the latter question concerns benchmarking trained models against a test dataset. This post provides details on how to implement large-scale Amazon SageMaker benchmarking and model selection tasks. swin-large-patch4-window7-224 195.4M efficientnet-b5 29.0M
AWS Machine Learning
JANUARY 10, 2023
We first benchmark the performance of our model on a single instance to identify the TPS it can handle per our acceptable latency requirements. Note that the model container also includes any custom inference code or scripts that you have passed for inference. Any issues related to end-to-end latency can then be isolated separately.
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